Introduction to Analytics and AI

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Introduction to Analytics and AI

Course Description

Analytics and artificial intelligence are important tools for understanding data, generating predictions, and making decisions. Through a series of recorded lectures led by a Georgia Tech faculty member and homework assignments, participants will enjoy an overview of many different types of analytics and AI models. They will learn how to choose or create the right models, datasets, and techniques to solve a problem or answer a particular question. Learners will gain an intuitive understanding of fundamental models and methods of analytics and AI, and practice implementing them using common industry tools over the course of the semester.

Course Content

Basics of Analytics and AI Modeling

  • Overview
  • Philosophy and ethics of modeling

Analytics/AI Models

  • Classification, clustering, regression, tree-based models, variable selection, design of experiments
  • Change detection, time series models
  • Decision-making and analysis using probability-based models, simulation, and optimization
  • Advanced models

Cross-cutting Concepts

  • Data and data preparation, outliers, dealing with missing data
  • Principal component analysis
  • Validation and testing
  • Measurement of data and model quality

Experiential Learning

  • Using software/libraries to implement models
  • Analysis and interpretation
  • Using and combining models and data
Requirements & Materials

Prerequisites

RECOMMENDED:

  • Some experience with R (statistical programming language)

REQUIRED:

  • Some Python programming proficiency
  • Basic calculus-based probability/statistics
  • Basic linear algebra

Materials

REQUIRED (Student must provide):

  • Internet connection
  • Free software (download and install before taking the course)
    • R statistical software (see cran.r-project.org) and R Studio (see rstudio.com/products/rstudio/download)
    • Arena simulation software (see www.arenasimulation.com/academic/students) for Windows, or SimPy (see pypi.python.org/pypi/simpy) for Windows/Mac
    • PuLP optimization software (see www.coin-or.org/PuLP) and/or Gurobi optimization software (see www.gurobi.com/academic-program-and-licenses)
    • Python programming language (see www.python.org)
    • Adobe Acrobat PDF reader (see get.adobe.com/reader/)
    • Honorlock proctoring software (see honorlock.com/)
  • Laptop or desktop computer (not a tablet)

PROVIDED (Student will receive):

  • All course lessons, assignments, and solutions

Who Should Attend

This course is designed for anyone interested in using a wide variety of analytics and AI models and tools to answer questions, make decisions, and solve problems. 

Computer science students coding on computers

What You Will Learn

  • Explanatory and predictive models for classification, clustering, change detection, time-series analysis, and various regression-based approaches (including feature selection)
  • Decision-support models such as simulation and optimization
  • Cross-cutting techniques like data preparation, validation, design of experiments, and dealing with missing data
  • Software/coding implementation of all models and techniques
  • Case studies for practicing how to use and combine analytics and AI models to reach comprehensive solutions
Analytics professional learning on computer and laptop

How You Will Benefit

  • Gain a high-level understanding of how analytics and AI models work and when it is appropriate (or inappropriate) to use each one.
  • Select an appropriate analytics/AI model to answer a question, specify the data you will need to solve it, and understand what the model’s solution will and will not provide as an answer.
  • Evaluate someone else’s use of analytics/AI to address a specific question, judge whether they have used an appropriate model and data, and determine whether their conclusion is reasonable.
  • Learn to think through descriptions and usage of new models, giving you a competitive advantage as new analytics and AI techniques are developed and used in practice.

 

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